From an agricultural point of view, deep learning models can be used in a variety of way to study the agricultural properties of soybean. Object detection can be performed using image or video data on phenotypic traits of soybean. In this project, a study on the phenotype analysis about soybean seed was conducted by artificial intelligence (AI) based on the YOLOv5 model. In model summary, layers and parameters were calculated as 243 and 7020913, respectively. Means of average precision (mAP)@[0.5: 0.95] was recorded as 0.835, 0.739, 0.785 for each class, and Daewonkong (DW) with yellow seed coat color was calculated as the highest value, and landrace with black seed coat color (NG2) revealed the lowest value. As a result of prediction performance in the confusion matrix, each class of DW, NG2, and inbreeding line with green seed coat color (NGT) showed significant correlation of true positive (TP) in the matrix with the same output value for the input value.

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Pathogens are the major causes of wheat crop yield losses, including the fungus Fusarium graminearum, an agent of Fusarium Head Blight (FHB). A better understanding of the relationship between plant morphological and biochemical traits and resistance to FHB can be effective in implementing a successful breeding program. This study investigated the relationship between FHB resistance as well as the morphological and biochemical traits in 20 durum wheat lines. Both morphological and biochemical traits were investigated using statistical tools. Therefore, analyses of variance, mean, as well as the correlation between the traits were con-sidered. In addition, for the morphological traits, cluster analyses were performed to identify similar genotypes in control and infected conditions. Furthermore, machine learning (ML) classification techniques, including Support Vector Machine (SVM), were proposed to detect the infected plants using morphological traits. The results show a great promise for the application of data-driven ML-based methods in plant breeding and disease detection.

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